Add to ORKGIn this paper we present a study of parallel and distributed genetic programming models and their relationships with the bloat phenomenon. The experiments that we have performed have also allowed us to find an interesting link between the number of processes, subpopulations and the model we should use when applying parallelism to GP. We study the synchronous and asynchronous version of the island-model in GP domain. I
A new parallel implementation of genetic programming based on the cellular model is presented and co...
The use of multiple populations in Genetic Programming is an area that is just beginning to be inves...
The parsimony pressure method is perhaps the simplest and most frequently used method to control blo...
This paper presents a new proposal for reducing bloat in Genetic Programming. This proposal is base...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
This paper examines the effects of relaxed synchronization on both the numerical and parallel effici...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. H...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
Analysing large-scale data brings promises of new levels of scientific discovery and economic value. ...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
Introduction The rapid growth of programs produced by genetic programming (GP) is a well documented...
AbstractWe present a formal model that allows to analyze non trivial properties about the behavior o...
The run-Time of evolutionary algorithms (EAs) is typically dominated by fitness evaluation. This is ...
Abstract Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the ...
A new parallel implementation of genetic programming based on the cellular model is presented and co...
The use of multiple populations in Genetic Programming is an area that is just beginning to be inves...
The parsimony pressure method is perhaps the simplest and most frequently used method to control blo...
This paper presents a new proposal for reducing bloat in Genetic Programming. This proposal is base...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
This paper examines the effects of relaxed synchronization on both the numerical and parallel effici...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. H...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
Analysing large-scale data brings promises of new levels of scientific discovery and economic value. ...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
Introduction The rapid growth of programs produced by genetic programming (GP) is a well documented...
AbstractWe present a formal model that allows to analyze non trivial properties about the behavior o...
The run-Time of evolutionary algorithms (EAs) is typically dominated by fitness evaluation. This is ...
Abstract Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the ...
A new parallel implementation of genetic programming based on the cellular model is presented and co...
The use of multiple populations in Genetic Programming is an area that is just beginning to be inves...
The parsimony pressure method is perhaps the simplest and most frequently used method to control blo...